In this paper, we propose a novel method, tracklet booster, that directly targets the two types of association error to boost the performance on IDF1 score, without tackling much for the detection error. Also, it can be efficiently plugged into any existing trackers. The tracklet booster has two main modules, Splitter and Connector. Splitter aims at finding potential ID-switch positions of a tracklet and split the tracklet into small pieces at the detected ID-switch positions. Splitter is designed with stacked dilated temporal convolution blocks to measure the temporal consistency of a tracklet. An adaptive label smoothing strategy with Gaussian kernels is proposed to improve the stability of the model training. With Splitter, the first type of error that multiple objects are assigned with the same ID can be largely reduced. On the other hand, Connector is introduced to address the second type of association error that the same identity is assigned to multiple tracklets. Connector aims at distinguishing different objects and merging multiple tracklets into one if they are from the same object. Specifically, Connector is modeled as a tracklet embedding network. Tracklets with small embedding distances are grouped and associated with the same tracking ID. Inspired by the transformer [1]}, we use the multi-head self-attention mechanism to learn the tracklet embedding. The framework of the tracklet booster is shown in Figure REF .
